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Machine Learning for Supporting Diagnosis of Amyotrophic Lateral Sclerosis Using Surface Electromyogram

contributor authorXu Zhang
contributor authorBarkhaus, Paul E.
contributor authorRymer, William Z.
contributor authorPing Zhou
date accessioned2020-03-12T18:27:11Z
date available2020-03-12T18:27:11Z
date issued2014
identifier issn1534-4320
identifier other6587612.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/958999?show=full
formatgeneral
languageEnglish
publisherIEEE
titleMachine Learning for Supporting Diagnosis of Amyotrophic Lateral Sclerosis Using Surface Electromyogram
typeJournal Paper
contenttypeMetadata Only
identifier padid7991509
subject keywordsdiseases
subject keywordselectromyography
subject keywordsfeature extraction
subject keywordslearning (artificial intelligence)
subject keywordsmedical signal processing
subject keywordsneurophysiology
subject keywordssensitivity
subject keywordssignal classification
subject keywordsstatistical analysis
subject keywordsEMG amplitude histogram kurtosis
subject keywordsEMG crossing-rate expansion kurtosis
subject keywordsamyotrophic lateral sclerosis diagnosis
subject keywordsclinical neurophysiology
subject keywordsclustering index
subject keywordsdiagnostic markers
subject keywordsdiagnostic sensitivity
subject keywordsdiagnostic specificity
subject keywordsinput feature sets
subject keywordslinear discriminant analysis classifier
subject keywordsmachine learning
subject keywordsneedle ele
identifier doi10.1109/TNSRE.2013.2274658
journal titleNeural Systems and Rehabilitation Engineering, IEEE Transactions on
journal volume22
journal issue1
filesize1180485
citations0


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